What are finite horizon look-ahead policies in reinforcement learning?
Per this paper a look ahead policy is a policy that will make decisions based on some 'horizon'. Here horizon means some time steps into the future, and so a finite horizon is simply a finite amount of time steps into the future. For example, as we are typically concerned with maximising returns in RL, a 10-step look ahead policy would choose an action at time $t$ that maximises the (expected) rewards at time $t+1, ... t+10$.